Search of nas data through association of errors

ABSTRACT

A computer-perceptible search input, whether typed, spoken, based upon machine vision, detection and/or interpretation of gestures, for example, may be received by a computing device from a single user. The received input by the single user may be matched with one or more stored digital items based upon prior inputs by the single user that previously led the single user to access the digital item(s). That is, it may be determined whether the received input is the same or similar to a previous input or inputs that led the computing device to search for, select and present digital items that were subsequently accessed (e.g., opened) by the user, which action signifies a successful search.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. patent applicationSer. No. 14/099,863 (issued as U.S. Pat. No. 9,864,781) filed on Dec. 6,2013, entitled “SEARCH OF NAS DATA THROUGH ASSOCIATION OF ERRORS,” whichclaims priority to U.S. Provisional Patent Application Ser. No.61/900,259 entitled “IMPROVED SEARCH OF NAS DATA THROUGH ASSOCIATION OFERRORS,” filed Nov. 5, 2013, the disclosures of which are herebyincorporated by reference in their entireties.

BACKGROUND

Searches may be carried out via the filename or by searching viakeywords in the metadata or within the file itself. Google®, forexample, leverages its massive databases of search terms gathered frombillions of searches by countless individuals to return a probablematch.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of one embodiment.

FIG. 2 illustrates populating an association database with indicia ofcomputer-perceptible search inputs and weights associated with thestored indicia, according to one embodiment.

FIG. 3 is a block diagram illustrating aspects of a computing deviceand/or storage server such as a Network Attached Storage (NAS),according to one embodiment.

FIG. 4 shows an exemplary user interface that illustrates aspects of oneembodiment.

FIG. 5 is a flowchart of a method according to one embodiment.

FIG. 6 is a flowchart of a method according to one embodiment.

DETAILED DESCRIPTION

While keyword searching is widespread, there is no comparable technologyin the personal user data space that leverages user errors. For example,there is no good and efficient way to search one's network attachedstorage (NAS) device for a specific file, picture, piece of music, movieclip, etc. that uses errors in previous search terms by the user togenerate better search results.

An embodiment comprises improved searching of data (such as personalstored on the user's NAS or direct attached storage (DAS) or such asother content accessible over the Internet) that is enabled through anassociation of search keywords, including errors or user freeassociations, with the stored files. In trying to find items, humansmake errors or pursue unpredictable paths based upon their personality,life experiences and prior experience in finding the same or similaritems. According to one embodiment, by using such “errors” (includingseemingly wholly unrelated search terms), a more efficient and highlypersonal search is made possible.

One embodiment comprises searching for stored digital items through thereceipt and processing of computer-perceptible user input. Such digitalitems may comprise files of any kind, digital data objects, documents,stored pictures, videos and/or audio content. According to oneembodiment, the phrase “computer-perceptible user input” encompasses alluser input that is in any way detectable, interpretable, or otherwiseperceptible to a computing device, such as text input, voice input,gestural input or input through machine vision, for example.

One embodiment comprises searching the user's NAS or othernetwork-accessible storage (or local storage) for stored digital itemsbased upon the user's past search terms or any othercomputer-perceptible user input received by a computing device toinitiate or refine a search for that or similar digital items. Theresult may be personal to that user, in that other users searching theirNAS or other storage with the same search terms or other forms of userinput may not be presented with the same search results.

One embodiment associates the user's keywords, errors and any otherpersonal association with the digital item or items retrieved orotherwise accessed as a result of those keywords, errors and/or personalassociations. For example, when searching for stored architecturalplans, the user might use the search terms “architecture, plans, house,fishing pond” in an effort to find the architectural plans for a guesthouse. In this context, the search terms “Architecture”, “plans” and“house” are readily understandable by most users as search terms likelyto cause the retrieval of architectural plans. Such keywords may beconsidered to be impersonal, in that they represent search terms thatany individual is more likely than not to input to cause the retrievalof architectural plans. Such likely search terms may have beenpreviously determined over many searches by many individuals and may, inthe aggregate, represent a relatively high statistical likelihood of notonly being the search terms a generic user is likely to enter, butsearch terms that are likely to retrieve content that is directlyassociated with the search terms “architecture”, “plans” and “house”.

The phase “fishing pond”, on the other hand, is not immediately orintuitively associated with architectural plans. Such aseemingly-unrelated association, according to one embodiment, maynevertheless be used to good advantage to cause the computing device tosearch and present the desired digital item—in this case, thesought-after architectural plans. The search terms “fishing pond”, inthis example, may not make sense to anyone other than the user. However,in that user's mind, the term “fishing pond” may have a directassociation with the architectural plans he or she is seeking. Forexample, the remodeled guest house that is the subject of thearchitectural plans may look out over the fishing pond, which is thefirst thing this particular single user thinks of when thinking of theremodeled guest house. However, other users would be unlikely to formthe same association and/or cause a similarly constituted computingdevice to retrieve the same digital item when presented with the samesearch terms. That is because such other user or users may not haveformed the same association of “fishing pond” with the architecturalplans being sought—and have previously used such search terms inconjunction with a prior search for the architectural plans. Theseassociations may be intentional or wholly unintentional. Herein, suchunintentional associations may be called “errors”. For example, had theuser previously entered “vacuum cleaner” instead of “fishing pond” whenpreviously searching for the architectural plans, the phrase “vacuumcleaner” would, according to one embodiment, become associated with thesought architectural plans such that, upon a subsequent search, enteringthe phrase “vacuum cleaner” would be likely to increase the likelihoodthat the architectural plans in question would be retrieved or at leastpresented to the user. Subsequent uses of such search terms or otherforms of input may strengthen the association between the object of thesearch and the entered search term or received other forms of input.Indeed, according to one embodiment, subsequent successful searchesusing such a personal association (i.e., searches using “vacuum cleaner”in which the architectural plans were retrieved by the computing deviceand accessed by the user) tend to reinforce the association between thearchitectural plans and the search terms, including “vacuum cleaner”.Such reinforcement may take the form of, for example, a weight, acoefficient or any other physical, mathematical or logical device thatoperates to quantify a variable degree of association between anyparticular user input and the digital item (e.g., file) being sought.

According to one embodiment, when a digital item such as a file isretrieved as a result of a search based on keywords, errors or otherassociations, a probability or weight may be increased. In this manner,the association between, say, “fishing pond” or “vacuum cleaner” and thearchitectural drawing file may be strengthened such that the file ismore likely to be returned the next time the user uses that or thosesearch terms, keywords, errors and/or associations. According to oneembodiment, therefore, no distinction need be made between words,phrases or any other computer-perceptible user input that aresemantically-related to the digital item being sought (i.e.,“architecture” is semantically related to architectural drawings) andwords, phrases or any other computer-perceptible user input that are not(or that would not be to the general public) semantically-related to thedigital item being sought (i.e., “vacuum cleaner” is not semanticallyrelated to architectural drawings, at least to the general public).According to one embodiment, computer-perceptible user input(s) that arereceived by the computing device that performs the search may beassociated, in the mind of the user, to the digital item(s) that is orare the subject of the search. According to one embodiment, indicia ofsuch computer-perceptible user input may be stored and assigned aweight. Upon a subsequent search, should the user again use suchseemingly unrelated (except to him or her) terms or phrases as searchterms, and access the same presented digital item, the associationtherebetween may be strengthened by, for example, increasing a weightassigned to the stored indicia of such computer-perceptible user input.Over time, therefore, the likelihood of the user retrieving specificdigital items based upon his or her unique associations will becorrespondingly increased, although a casual, fly-on-the-wall observermay not understand why “vacuum cleaner” causes the computing device topresent architectural plans to the user.

FIG. 1 is a block diagram of one embodiment. As shown therein, a network114 may couple a computing device comprising a NAS 110 or other DirectAccess Storage (DAS) with one or more computing devices 102, 106, 108(e.g., smart phone, tablet, laptop, desktop, smart TV, media player,etc.) that may be configured to access the computing device 110 over thenetwork 114. In particular, one or more of the computing devices 102,106 and 108 may be configured to carry out searches of the digital itemsstored or otherwise accessible to the NAS (or other computing device)110. One or more of the computing device 102, 106 and 108 may compriselocal storage, as shown at 104. As shown in the example of FIG. 1, auser may enter, say or otherwise provide the computing device 108 withcomputer-perceptible user input “dog ran away”, “vacation” and“countryside”. While one may infer (rightly or wrongly), from the searchterms “vacation” and “countryside”, that the user is searching forpreviously-stored content related to a prior vacation in the country,the search terms “dog ran away” may only be personally evocative ofpredetermined content for that particular user, or perhaps his or herfamily. According to one embodiment, if the search terms “dog ran away”,“vacation” and “countryside” cause the computing device 110 to retrieveand present a desired digital item (e.g., a movie or a photo) and theuser accesses (e.g., opens) the presented digital item(s), then theweight or association of these search terms, including theseemingly-unrelated “dog ran away” phrase will be strengthened, suchthat subsequent searches using “dog ran away” as search terms will bemore likely to find and present to the user the previously found andaccessed digital item. As such, the searches may become highly personal,as “fishing pond” and “architectural drawings”, at first blush, bear noassociation to one another as developed in the previous example—exceptfor the person to whom such association makes perfect sense. In thisregard, there may be no “errors” per se.

According to one embodiment, a probabilistic learning algorithm may beemployed by the NAS 110 (e.g., computing device or storage server) to“train” the computing device to return the desired search results. Sucha probabilistic learning algorithm, represented in FIG. 1 at 120, maycomprise, for example, an implementation of Bayes' theorem:P(A|B)=[P(B|A)P(A)]/P(B), which quantifies the probability of a file (orother digital item) A being found as a result of computer-perceptibleuser input B being entered as search term(s). The result is a numberthat represents the probability of finding the file A in the user's NASor other storage given the computer-perceptible user input (keywords,“errors”, highly personal search terms, and the like) B entered.

According to one embodiment, the NAS 110 may also employ animplementation of neural networks (NN) as shown at 118 or similarlearning functionality using, for example, a threshold function or asmooth non-linear activation function, together with weights that areadjusted upwards when a search is successful and downwards when thesearch terms, associations do not result in the user retrieving orotherwise accessing the desired document or digital item.

FIG. 2 illustrates populating an association database with indicia ofcomputer-perceptible search inputs and weights associated with thestored indicia, according to one embodiment. In some embodiments, theassociation database may comprise a relational database, key/valuepairings, flat file, XML file, and/or other data repository. Theassociation database 112 may, as shown in FIG. 1, be stored within theNAS 110. Alternatively, the association database may be stored remotelytherefrom, such as in the local storage 104 coupled to computing device104, for example. According to one embodiment, the association database112 may be configured to be accessible over the network 114 and/or maybe distributed across several devices. As shown, the associationdatabase or index file 112 may be configured, according to oneembodiment, to store metadata 408 embedded or otherwise associated withthe digital item. In this example, the digital item is a digitalpicture, baddayforkevin.jpg. The association database 112 may store,according to one embodiment, computer-perceptible search inputs from asingle user that previously led that single user to access (e.g., open)the associated digital item. For instance, that single user may havepreviously have entered search inputs “dog”, “countryside”, “4^(th)”,“vacation” and “lake”. If baddayforkevin is a picture of, for example,the user's child Kevin struggling to learn how to swim during a vacationat Lake Tahoe on the 4^(th) of July, such search terms are readilysemantically or at least thematically-related to the name and/orcontents of the picture or to the information with which this digitalitem was tagged. These search terms may be stored in the associationdatabase 112. Also, other computer-perceptible search inputs may bestored in the association database 112 such as “butter”, “dog” and “dogran away”, which seemingly bear no readily apparent relation to thebaddayforkevin JPG digital picture. Moreover, such search terms may bewholly unrelated to the original metadata associated with thebaddayforkevin.JPG digital picture. For example, unrelated search termscan include search terms that do not match the name of a file, are notfound in the contents of the file, and/or are not found in the originalmetadata of the file. However, such seemingly-unrelated associations mayhave been made by the specific user on previous accesses of this digitalitem. For example, the user may remember that there was no butter forhis morning toast that day, and that the family dog ran away theafternoon of the day Kevin was learning to swim. Those aspects may bethe most memorable, in the user's mind, of the associations the user mayhave made with this particular picture. Advantageously, as the system,in one embodiment, does not need to rely on the contents of files togenerate associations, the system can generate matches for files whosecontents are not ordinarily text-searchable, such as picture files,audio files, movie files or encrypted files. As shown, previous entriesof the computer perceptible search inputs “butter” and “dog ran away”during a prior search may have caused the computing device to presentthe digital item baddayforkevin.jpg to the user, which the usersubsequently opened as that picture was indeed the object of his or hersearch.

This accessing of the presented digital item, according to oneembodiment, operates to strengthen the association between “butter” and“dog ran away” and the picture of Kevin swimming. One way to strengthenthis association is to increase the value of coefficients or weightsassociated with each of the computer-perceptible search inputs. In thismanner, as shown in FIG. 3, the weights associated with “butter”,“vacation” and “dog ran away” may be increased, which increases thelikelihood that the search engine in the NAS or other implementingcomputing device will subsequently return this digital item when nextpresented with similar or the same FIG. 4 shows an exemplary userinterface that illustrates aspects of one embodiment. Weights or othercoefficients representative of an association of search terms or otherforms of computer-perceptible inputs may be weakened or otherwisedecreased through non-use. That is, the weights associated with thosesearch terms that have not been used or not used successfully, may bedecreased and/or replaced with other associations as the search enginerefines its search methodologies for this particular user.

According to one embodiment, therefore, the search becomes moreefficient the longer the user uses it, as probabilities are increased ordecreased and/or as weights and/or associations are changed dependingupon the success or failure of prior searches. To do so, the associationdatabase 112 or index file may be consulted during searches, to carryout and/or refine the search by matching or determining the similaritybetween the computer-perceptible user inputs and the indicia ofassociations, metadata and/or other search terms stored therein.

FIG. 3 is a block diagram of a computing device 102, 106, 108, 110 or112 according to one embodiment. The computing device may comprisetangible, non-transitory storage comprising, for example, rotary mediastorage comprising magnetic disks 302, non-volatile semiconductor memory304, or a hybrid thereof 303, comprising both magnetic disks 302 andnon-volatile semiconductor memory 304. The computing device may alsocomprise volatile memory 306 and a processor (controller,microprocessor) 308. The processor 308 may be coupled to the memory 306,as well to the non-volatile memory or memories 302, 303 and 304.According to one embodiment, the processor 308 may be configured toexecute sequences of instructions configured to carry out the methods ofFIGS. 5 and 6, discussed hereunder.

FIG. 4 shows an exemplary user interface that illustrates aspects of oneembodiment. The exemplary user interface 400 may be configured as abrowser or as a mobile app, among other possibilities. As shown, theuser may log in and become authenticated by the application, as shown at402. In this manner, the user's (John's) previous search history(including search terms, keywords, associations and other forms ofcomputer-perceptible search inputs) may be accessed and used to presentthe most relevant search results to this single user (John). Other usersmay be associated with their own instance of the association database112 and may have made and caused to be stored therein entirely differentassociations for the same stored content. In a search bar 404, the usermay enter his computer-perceptible search inputs. In this exemplarycase, the user John has entered “dog ran away”, “vacation” and“countryside”, as these are John's most memorable associations with thedigital picture (baddayforkevin.jpg) for which he is searching. At 408,the NAS 110 or other computing device returns with a movie called 2004Tahoe.mov, the file baddayforkevin.jpg and one or more websites or deeplinks to a document accessible at a website. As shown, one embodimentmay return search results not only from the user's own NAS or othernetwork-accessible or local storage, but also from other website andother Wide Area Network (WAN) accessible content. With the properpermissions, another user's NAS or network-accessible storage may besearched and the results thereof presented to the user, as shown at 408.At reference numeral 410, the user may be presented with the opportunityto select and open one of the presented digital items or to attempt torefine his or her search (or start over), as suggested at 412. Otheruser interface implementations are possible.

FIG. 5 is a flowchart of a method according to one embodiment. As showntherein, block B51 calls for receiving an input from a single user. Theinput may, according to one embodiment be any computer-perceptiblesearch inputs be it typed, spoken, based upon machine vision, detectionand interpretation of gestures and the like. At B52, the received inputby the single user may be matched, through access to an associationdatabase, for example, with one or more stored digital items based uponprior inputs by the single user that previously led the single user toaccess the digital item(s). That is, it may be determined whether thereceived input is the same or similar to previous inputs that led thecomputing device to search for, select and present digital items thatwere subsequently accessed (e.g., opened) by the user, which action maybe associated with a successful search. At B53, the digital item(s)selected as a result of the search may be presented the single user.According to one embodiment, the digital item(s) may be stored, forexample, in one or more NAS, DAS or other forms of network-accessiblestorage. As detailed above, the input from the single user received inB51 in FIG. 5 may comprise search terms that are personal to that singleuser and/or may be or include search terms that are unrepresented by thename of the digital item or by the content thereof. The input from thesingle user received in B51 in FIG. 5 may also comprise search termsthat are unrepresented by the original metadata of the stored digitalitem.

According to one embodiment, one or more of the search terms orcomputer-perceptible search inputs may be or comprise a search term orcomputer-perceptible search input that was previously entered butsubsequently deleted prior to submission. That is, a user may haveentered one or more search terms or have submitted some othercomputer-perceptible search inputs in a prior search for the samecontent, but subsequently changed his or her mind and deleted the searchterms or have submitted some other computer-perceptible search inputsprior to submitting the same to the search engine to initiate or refinethe search. Such inputs may provide contextual cues of the content forwhich the user is searching. Accordingly, even though such search termsor computer-perceptible search inputs was or were never submitted (e.g.,by pressing “enter”, for example), indicia of such search terms orcomputer-perceptible search inputs may, nevertheless, be stored in theassociation database 112 or otherwise used in subsequent searches forthe same or similar content. Indeed, such deleted or entered but neversubmitted search terms or computer-perceptible search inputs may be thefirst thing the user thinks of when searching for this subject matter(even though he or she may later change his or her mind and decide notto submit these to the search engine) and, as such, may constitute anactionable (by the computing device) indication of the searched content.

As noted above, block B52 calls for the received input by the singleuser to be matched with one or more stored digital items based uponprior inputs by the single user that previously led the single user toaccess the digital item. Such matching may, according to one embodiment,may comprise determining the similarity between the received input fromthe user and the prior inputs received from the single user prior to aprevious access thereto by the single user. The degree of suchsimilarity may be determined according to any of a number of matchingalgorithms. Moreover, the threshold of similarity that would trigger thepresentation of the digital items or items (e.g., a document, a file,movie, a picture, music, a website or location) in block B53 may befreely set, according to the implementation. According to oneembodiment, the prior inputs (received from the single user prior to aprevious access thereto by the single user) may be determined over apredetermined time period, or may span any time period during which apredetermined number of user inputs are received. For example, theuser's behavior may be tracked after say, an unsuccessful search. If,for example, the user opened a particular file by navigating directly tothe file via a file utility or a word processing application (forexample) within a predetermined time period after an unsuccessfulsearch, the association between the just-opened file and the previouslyreceived user inputs may be strengthened. This may then increase thelikelihood that this file will be presented to the user in the futureupon subsequent receipt of the same user inputs of the previouslyunsuccessful search. To track previous user inputs, association indiciaof such prior inputs by the single user may be stored in the associationdatabase 112, or equivalent. As graphically illustrated in FIG. 2, aweight may be associated with each of the stored association indicia.This weight may, according to one embodiment, be increased upon the useraccessing (e.g., opening, viewing) the presented digital item or itemsand decreased when the user does not access the presented digitalitem(s).

FIG. 6 is a flowchart of a method of accessing a digital item, accordingto one embodiment. As shown therein, the method may comprise initiatinga search based upon receiving computer-perceptible search inputs from asingle user, at least one of the computer-perceptible search inputsbeing unrelated to, for example, the name and/or content of the storeddigital item that is the object of the search, as shown at B61. Thecomputer-perceptible search input(s) may also be unrelated to, forexample, the original metadata of the digital item being sought. BlockB62 calls for determining the similarity of the receivedcomputer-perceptible search inputs with one or more previously-stored(in association database 112, for example) indicia ofcomputer-perceptible search inputs that were received from the singleuser prior to the single user previously accessing the stored digitalitem. At B63, the stored digital item(s) may be presented to the singleuser when the determined similarity is greater than a predeterminedthreshold.

Advantageously, embodiments enable efficient searching of a user's NASor other accessible storage based on the user's own subjective, personalassociations or actions, even if such associations or actions bear noreadily apparent relation (to other users, for example) to the object ofthe search. Indeed, how users find their data is often very personal.Embodiments do not force the user into some pre-conceived framework forsearching his or her own personal data on his or her NAS or othernetwork-accessible storage. Such personalized searches may be fasterthan searching in the conventional manner and may encourage sharing ofpersonal pictures, movies with others.

While certain embodiments of the disclosure have been described, theseembodiments have been presented by way of example only, and are notintended to limit the scope of the disclosure. Indeed, the novelmethods, devices and systems described herein may be embodied in avariety of other forms. Furthermore, various omissions, substitutionsand changes in the form of the methods and systems described herein maybe made without departing from the spirit of the disclosure. Theaccompanying claims and their equivalents are intended to cover suchforms or modifications as would fall within the scope and spirit of thedisclosure. For example, those skilled in the art will appreciate thatin various embodiments, the actual physical and logical structures maydiffer from those shown in the figures. Depending on the embodiment,certain steps described in the example above may be removed, others maybe added. Also, the features and attributes of the specific embodimentsdisclosed above may be combined in different ways to form additionalembodiments, all of which fall within the scope of the presentdisclosure. Although the present disclosure provides certain preferredembodiments and applications, other embodiments that are apparent tothose of ordinary skill in the art, including embodiments which do notprovide all of the features and advantages set forth herein, are alsowithin the scope of this disclosure.

What is claimed is:
 1. A computing device, comprising: a memory; and aprocessor coupled to the memory, the processor being configured toexecute instructions stored in the memory to: receive an input from auser, the received input comprising a search term; and identify adigital item corresponding to the received input based at least partlyon: the received input; prior accesses of the digital item based uponprior inputs of search terms by the user; and prior associations of thesearch terms of the prior inputs with prior search results comprisingthe digital item, wherein at least some of the search terms of the priorinputs are unrelated to a name, original metadata, and content of thedigital item and have previously led the user to find and access thedigital item.
 2. The computing device of claim 1, wherein search resultsare personal to the user such that another user using the same searchterm would be presented with a different digital item.
 3. The computingdevice of claim 1, wherein the search term was entered but subsequentlydeleted prior to submission.
 4. The computing device of claim 1, theprocessor further configured to: associate the search term with thedigital item by determining a similarity between the received input andthe prior inputs received from the user prior to a previous access ofthe digital item by the user.
 5. The computing device of claim 1,wherein the received input and the prior inputs further comprisecomputer-perceptible user actions.
 6. The computing device of claim 5,wherein the computer-perceptible user actions comprise at least one oftext input, voice input, gestural input, and machine vision input. 7.The computing device of claim 1, wherein the digital item comprises atleast one of a document, a file, a movie, a picture, a website, music,and a location.
 8. The computing device of claim 1, wherein the priorinputs are determined over a predetermined time period.
 9. The computingdevice of claim 1, wherein the processor is further configured to storeassociation indicia of the prior inputs.
 10. The computing device ofclaim 9, wherein the processor is further configured to associate aweight with each of the stored association indicia, the weight beingincreased responsive to determining that the user accesses the digitalitem and decreased responsive to determining that the user does notaccess the digital item.
 11. A computer-implemented method, comprising:receiving an input from a user, the received input comprising a searchterm; and identifying a digital item corresponding to the received inputbased at least partly on: the received input; prior accesses of thedigital item based upon prior inputs of search terms by the user; andprior associations of the search terms of the prior inputs with priorsearch results comprising the digital item, wherein at least some of thesearch terms of the prior inputs are unrelated to a name, originalmetadata and content of the digital item and have previously led theuser to find and access the digital item.
 12. The computer-implementedmethod of claim 11, wherein search results are personal to the user suchthat another user using the same search term would be presented with adifferent digital item.
 13. The computer-implemented method of claim 11,wherein the search term was entered but subsequently deleted prior tosubmission.
 14. The computer-implemented method of claim 11, furthercomprising: associating the search term with the digital item bydetermining a similarity between the received input and the prior inputsreceived from the user prior to a previous access of the digital item bythe user.
 15. The computer-implemented method of claim 11, wherein thereceived input and the prior inputs further comprisecomputer-perceptible user actions.
 16. The computer-implemented methodof claim 15, wherein the computer-perceptible user actions comprise atleast one of text input, voice input, gestural input, and machine visioninput.
 17. The computer-implemented method of claim 11, wherein thedigital item comprises at least one of a document, a file, a movie, apicture, a website, music, and a location.
 18. The computer-implementedmethod of claim 11, wherein the prior inputs are determined over apredetermined time period.
 19. The computer-implemented method of claim11, further comprising storing association indicia of the prior inputs.20. The computer-implemented method of claim 19, further comprisingassociating a weight with each of the stored association indicia, theweight being increased responsive to determining that the user accessesthe digital item and decreased responsive to determining that the userdoes not access the digital item.
 21. A computing device, comprising: amemory means; and a processing means coupled to the memory means, theprocessing means being configured to execute instructions stored in thememory means to: receive an input from a user, the received inputcomprising a search term; and identify a digital item in a data storagecorresponding to the received input based at least partly on: thereceived input; prior accesses of the digital item stored on the datastorage based upon prior inputs of search terms by the user; and priorassociations of the search terms of the prior inputs with prior searchresults comprising the digital item, wherein at least some of the searchterms of the prior inputs are unrelated to a name, original metadata andcontent of the digital item and have previously led the user to find andaccess the digital item.